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Date:         Wed, 5 Nov 2003 10:55:46 -0600
Reply-To:     Thompson Bill T Contr USAFSAM/FEC <Bill.Thompson@BROOKS.AF.MIL>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Thompson Bill T Contr USAFSAM/FEC <Bill.Thompson@BROOKS.AF.MIL>
Subject:      Re: Transforming data
Comments: To: Peter Flom <flom@NDRI.ORG>
Content-Type: text/plain; charset="iso-8859-1"

Peter,

You are correct in your assumptions and I will follow your lead regarding the residuals, skew and kurtosis. If the residuals are "normally" distributed would you suggest NOT transforming the data?

One issue that has come up regarding "eye" is that these are "fellow" eyes from the same person (humans). Since the results are "highly" correlated at baseline it was suggested we just pool the eyes and eliminate that variable. Unfortunately, subjects received surgery on one eye at a time with the possibility of several weeks between surgeries. As a result the followup data is not as highly correlated because of different surgical outcomes for different eyes. Hence, we are trying to decide if we should analyze each eye separately or use a mixed model.

Your thoughts.

Bill

-----Original Message----- From: Peter Flom [mailto:flom@NDRI.ORG] Sent: Tuesday, November 04, 2003 2:21 PM To: SAS-L@LISTSERV.UGA.EDU Subject: Re: Transforming data

Bill

If I understand yoiu correctly, you have measurements of one variable (light sensitivity) at each of 36 combinations of 4 other variables. Light senstivity is your DV, and time, light, screen and eye are your IVs (Please correct me if I am mistaken here).

In this case, you certainly cannot transform the DV in some cells and not others. But the key question is not whether the values in each of the 36 combinations is normally distributed, the question is whether the residuals from your model are normally distributed.

I have found that the best way of looking at this is graphcially, but I don't have SAS/Graph, and I usually do this in R. If you have SAS Graph, there is doubtless a good way to make some charts.

Alternatively, you can do tests and look at the skew and kurtosis of the residuals.

BTW, if 'eye' is left vs. right on the same person (or whatever sort of creature you are examining) then I would recommend you look at a mixed model, since this will be nested within person

HTH

Peter

Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St www.peterflom.com New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax)

>>> Thompson Bill T Contr USAFSAM/FEC <Bill.Thompson@brooks.af.mil> 11/4/2003 3:09:59 PM >>> Peter,

These 36 variables are actually part of a 3x3x2x2 repeated measures design.

time x light x screen x eye

These variables represent "contrast sensitivity" values taken at baseline, and then 12 months and 24 months post refractive surgery. The issue has been raised as to wether or not we should analyze the "contrast sensitivity" values or the "log contrast sensitivity" values. These are continuous data.

The data was analyzed as a repeated measures design described above using the "raw" contrast sensitivity data.


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